Metabolomics

, Volume 6, Issue 1, pp 27–41

Differential metabolomics software for capillary electrophoresis-mass spectrometry data analysis

  • Masahiro Sugimoto
  • Akiyoshi Hirayama
  • Takamasa Ishikawa
  • Martin Robert
  • Richard Baran
  • Keizo Uehara
  • Katsuya Kawai
  • Tomoyoshi Soga
  • Masaru Tomita
Original Article

DOI: 10.1007/s11306-009-0175-1

Cite this article as:
Sugimoto, M., Hirayama, A., Ishikawa, T. et al. Metabolomics (2010) 6: 27. doi:10.1007/s11306-009-0175-1
  • 367 Downloads

Abstract

In metabolomics, the rapid identification of quantitative differences between multiple biological samples remains a major challenge. While capillary electrophoresis–mass spectrometry (CE–MS) is a powerful tool to simultaneously quantify charged metabolites, reliable and easy-to-use software that is well suited to analyze CE–MS metabolic profiles is still lacking. Optimized software tools for CE–MS are needed because of the sometimes large variation in migration time between runs and the wider variety of peak shapes in CE–MS data compared with LC–MS or GC–MS. Therefore, we implemented a stand-alone application named JDAMP (Java application for Differential Analysis of Metabolite Profiles), which allows users to identify the metabolites that vary between two groups. The main features include fast calculation modules and a file converter using an original compact file format, baseline subtraction, dataset normalization and alignment, visualization on 2D plots (m/z and time axis) with matching metabolite standards, and the detection of significant differences between metabolite profiles. Moreover, it features an easy-to-use graphical user interface that requires only a few mouse-actions to complete the analysis. The interface also enables the analyst to evaluate the semiautomatic processes and interactively tune options and parameters depending on the input datasets. The confirmation of findings is available as a list of overlaid electropherograms, which is ranked using a novel difference-evaluation function that accounts for peak size and distortion as well as statistical criteria for accurate difference-detection. Overall, the JDAMP software complements other metabolomics data processing tools and permits easy and rapid detection of significant differences between multiple complex CE–MS profiles.

Keywords

Capillary electrophoresis–mass spectrometry Metabolome Data analysis Software 

Supplementary material

11306_2009_175_MOESM1_ESM.doc (36 kb)
Supplementary material 1 (DOC 36 kb)
11306_2009_175_MOESM2_ESM.ppt (325 kb)
Figure S1 Screenshots of some of the main functions of JDAMP: (A) File import window. (B) Results window for background subtraction and noise filtering. (C) Alignment setup window. Alignment results windows to visualize distributed peak locations (D) and the migration time-normalization function (E).(PPT 325 kb)
11306_2009_175_MOESM3_ESM.ppt (190 kb)
Figure S2 Baseline and noise structure in CE–MS data. Total ion electropherogram (A) and three examples of extracted electropherograms at 122.0964 m/z (B), 193.0681 m/z (C) and 307.1765 m/z (D) from mouse serum samples used in this study. The electropherograms were obtained using Analyst QS from raw files. The last three electropherograms were noise-reduced electropherograms at 122 m/z (E), 193 m/z (F), and 308 m/z (G) (corresponding to B, C, and D, respectively). (PPT 190 kb)
11306_2009_175_MOESM4_ESM.ppt (187 kb)
Figure S3 Performance of the migration time normalization procedure. The vertical lines in the box indicate upper median, median and lower median, and the whiskers indicate the maximum and minimum values. Migration times for the peaks matched between two samples before/after migration time normalization are depicted in (A–D). The plots (E) show all paired peaks assigned in the DP process, corresponding to (A). (PPT 187 kb)
11306_2009_175_MOESM5_ESM.ppt (144 kb)
Figure S4 Typical traces of the electric current applied to the capillary during each run. The blue and red trajectories represent different analyses of the same sample on the same instrument. The figures were generated using MassHunter software controlling an Agilent TOF–MS system. (PPT 144 kb)
11306_2009_175_MOESM6_ESM.ppt (2.8 mb)
Figure S5 Overlaid electropherograms of the results ranked between 13 and 50 based on calculations performed using the datapoint-by-datapoint t-score, smoothed t-score, ABSRel, area, or Gaussian area functions. The results ranked above 12 are shown in Fig. 5. The red and blue curves represent the peaks for the samples and control datasets, respectively. (PPT 2865 kb)
11306_2009_175_MOESM7_ESM.ppt (375 kb)
Figure S6 Graph (A) and (B) are the association between the ranks of differences within top 50th order detected by MathDAMP and JDAMP based on ABSRel (A) and smoothed t-score (B). The missing data indicate corresponding results were not assigned within top 50 ranking by the other tool. Graphs (C), (D), and (E) were electropherograms at 112 m/z, 567 m/z, and 132 m/z detected by MathDAMP based on ABSRel (C and D) and smoothed t-score (E), respectively. The blue and red lines denote controls and samples, respectively. (PPT 375 kb)
11306_2009_175_MOESM8_ESM.ppt (718 kb)
Figure S7 Processing results of standard solution with additional spiking (+50%) of three metabolites using MZMine. Panel (A) shows the peak detection results around N-α-Benzenolarginine ethylester (307 m/z) in one of the datasets, and panel (B) shows the aligned peak location of six datasets. Although the two black rectangles were not detected, the intersect of the red lines was detected as a peak, as shown in the table in (A). Panels (C) and (D) depict the datasets used for (A) in a 3D representation showing the full range (C) and between 100 m/z and 1000 m/z (D), respectively. Table (E) shows the portion of the aligned matrix related to peaks for 2,4-Dimethylaniline (122.0964 m/z). The red rectangles are expected to be aligned. Figures (F) and (G) depict the extracted electropherograms binned between 122.09 m/z and 122.10 m/z. The red triangle indicates the top of the 2,4-Dimethylaniline peak (only shown in misaligned electropherograms). Panels (A), (B), (E), (F) and (G) are screen-shots of MZMine (Katajamaa, et al., 2006), and figures (C) and (D) were generated using MZMine ver.2 (Okinawa Institute of Science and Technology, http://mzmine.sourceforge.net/). (PPT 718 kb)
11306_2009_175_MOESM9_ESM.xls (24 kb)
Supplementary material 9 (XLS 25 kb)

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Masahiro Sugimoto
    • 1
    • 2
  • Akiyoshi Hirayama
    • 1
  • Takamasa Ishikawa
    • 3
  • Martin Robert
    • 1
  • Richard Baran
    • 1
    • 4
  • Keizo Uehara
    • 2
  • Katsuya Kawai
    • 1
    • 2
  • Tomoyoshi Soga
    • 1
    • 3
  • Masaru Tomita
    • 1
    • 3
  1. 1.Institute for Advanced BiosciencesKeio UniversityTsuruokaJapan
  2. 2.Department of BioinformaticsMitsubishi Space Software Co. LtdAmagasakiJapan
  3. 3.Human Metabolome Technologies IncTsuruokaJapan
  4. 4.Life Sciences Division, MS: 84R0171Lawrence Berkeley National LaboratoryBerkeleyUSA

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